Analysing the Spread of Cyber-Attacks in Computer Networks: A Simulation Study

Analysing the Spread of Cyber-Attacks in Computer Networks: A Simulation Study
  IJETT-book-cover           
  
© 2023 by IJETT Journal
Volume-71 Issue-8
Year of Publication : 2023
Author : Shiju Rawther, S. Sathyalakshmi.
DOI : 10.14445/22315381/IJETT-V71I8P203

How to Cite?

Shiju Rawther, S. Sathyalakshmi., "Analysing the Spread of Cyber-Attacks in Computer Networks: A Simulation Study," International Journal of Engineering Trends and Technology, vol. 71, no. 8, pp. 26-38, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I8P203

Abstract
Cyber-attack propagation in computer networks is a critical concern in network security. This study adopts a simulation approach to investigate the spread of cyber-attacks, drawing inspiration from the Kermack-McKendrick model, which models the spread of epidemic diseases. Furthermore, the study incorporates the learning effect by leveraging machine-learning techniques for intrusion detection in computer networks. The review of references encompasses a comprehensive exploration of machine learning-based intrusion detection systems, considering various algorithms such as support vector machines, genetic algorithms, and deep learning architectures. Additionally, the review delves into the application of machine learning techniques in detecting specific threats, including distributed denial-of-service (DDoS) attacks, botnet activities in cloud computing environments, and intrusions in the Internet of Things (IoT). Several references highlight the effectiveness of anomaly detection techniques, encompassing clustering, classification, and deep learning methods. Notably, the survey examines the UNSW-NB15 network dataset, which serves as a benchmark for evaluating intrusion detection algorithms. Incorporating fuzzy data mining, hybrid machine learning approaches, and optimized algorithms further enhances the accuracy and efficiency of intrusion detection systems.
The review also sheds light on the challenges associated with intrusion detection using machine learning, including the availability of suitable datasets, feature selection, and algorithm scalability. By analyzing the state-of-the-art machine learning techniques for network intrusion detection, the study establishes a taxonomy of approaches and identifies key research trends. Overall, this study presents a simulation-based investigation of cyber attack propagation, employing the KermackMcKendrick model. It further incorporates machine learning techniques to enhance intrusion detection in computer networks.
The review of references provides valuable insights into the application of machine learning algorithms and their effectiveness in combating cyber threats. The study contributes to the development of proactive defense strategies and establishes a foundation for future research in network security. The propagation of cyber-attacks in computer networks poses significant threats to information security and system integrity. This paper presents a simulation study that focuses on analyzing the spread of cyber-attacks using the Kermack-McKendrick model, which is widely used in epidemiology to study the dynamics of infectious diseases. In addition, the study incorporates the learning effect, considering that nodes in the network can acquire temporary immunity or enhanced defenses over time. The simulation results provide valuable insights into the propagation patterns and dynamics of cyber-attacks, highlighting the importance of considering the learning effect in modeling the spread of such attacks. The findings contribute to developing effective strategies for network defense and incident response.

Keywords
Kermack-McKendrick model, Cyber-attack, Network, Payoff, Equilibrium, Star network, Attack-link formation, Propagation Dynamics.

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